How can we help keep kids healthy so they can focus on being kids rather than on being sick? One solution is to incorporate artificial intelligence (AI) into pediatric care.
With help from AI, healthcare providers can predict diseases and other health issues in children, improve care and achieve better outcomes. Here’s a closer look at how AI can help the future of pediatric care.
Keep kids healthy with help from predictive analytics
Of the 30 million Americans living with a rare disease, children account for more than half – that’s more than 15 million children in the US alone. [iii]
What if healthcare providers could reduce this number by identifying children at high risk of developing a disease and work to prevent disease onset? AI makes this possible.
Artificial intelligence can positively impact pediatric care by providing predictive analytics around various diseases and ailments. Lumiata’s clinical data models, for example, use AI and machine learning (ML) to analyze health records, search for risk factors, and predict the onset of diseases in patients within the next 12 months. This includes predicting the onset of asthma, obesity, type 2 diabetes, and other illnesses in children.
Healthcare providers can then use these predictive analytics to improve clinical decision-making and guide risk-based care management design. When providers identify children who are at high risk of developing a disease, healthcare professionals can provide more specialized care and ensure they’re addressing each child’s specific risk factors. By adjusting the child’s care plan and doing so promptly, providers may be able to improve the child’s health and prevent the onset of diseases.
By using artificial intelligence to gain valuable insights and improve the quality of care, providers can achieve better outcomes for children.
Predict and prevent hospitalizations
More than 3 million children are hospitalized each year in the US. [iv] By incorporating AI tools into pediatric care, providers can help keep kids healthy and out of hospitals. For example, providers can use Lumiata’s AI products to identify patients with risk factors that could lead to diseases if left untreated. Providers can then address these risk factors to help prevent disease onset and the need for hospital stays.
In the event a child develops a disease, Lumiata can provide insight into disease-related complications. This improves providers’ ability to look out for complications and help prevent patients’ conditions from getting worse. By helping children avoid complications, providers can also help children avoid hospital admissions.
Lumiata can also predict hospitalizations, ICU admissions, and readmissions within the next 12 months. With this information, providers can plan for surges in the demand for care and make sure they have enough staff and resources available to provide quality care to everyone who needs it.
Monitor care utilization
Another way that artificial intelligence can improve pediatric care is by helping providers quickly and easily analyze healthcare data, including data pulled from electronic health records. With the right AI tools, providers can review care utilization to see if children are going to their annual check-ups and see any other medical services a child is being provided.
Providers can also review a patient’s health conditions and any prescriptions that were written. The ability to easily access and review this information gives providers a clearer understanding of the child’s health status and care history, which can guide the decisions the provider makes and the care they provide going forward.
Make quality care more accessible
As healthcare needs and goals continue to change, artificial intelligence can support providers’ efforts to provide quality care for their patients and achieve better outcomes. AI can help providers reach people who may not typically have access to quality care, such as people in low-income and middle-income countries (LMICs) and people who have difficulty getting to in-person doctors’ appointments.
In LMICs, mothers and children receive less than half of the recommended clinical actions in a typical curative or preventative visit, and diagnoses for serious conditions are often incorrect. [v]
Artificial intelligence could help pediatric care providers in LMICs more accurately diagnose health conditions, predict diseases, and identify risk factors, allowing them to more clearly understand the needs of their patients so they can provide better care. Lumiata’s AI products can be used to achieve this.
Lumiata is also useful when building telemedicine strategies. Telemedicine and telehealth services help pediatric care providers reach patients without requiring in-person visits. Our AI tools help providers identify at-risk patients so they can reach out to patients before major
health events. By intervening earlier and remotely, providers can help patients before critical in-person care is needed and make care more accessible.
How it works
Lumiata’s vision is to democratize AI in healthcare to reduce costs and improve the quality of care. To accomplish this, we make it easier for healthcare providers to quickly and thoroughly analyze their data and draw out actionable insights.
We start by importing your EHR and other data into our ecosystem and preparing your data for machine learning. This includes performing data quality checks, validating and normalizing your data, and cleansing your data. We also run your data through our disease tagging system to identify key indicators of chronic and catastrophic conditions linked at the individual level. We also enrich your data with our own, which is derived from 130 million patient records, 35,000 physician curation hours, and 50 million published articles.
Finally, we create Lumiata Person360 records, longitudinal records for each person in your data. With your data now ready for machine learning, you can use our AI tools to make predictions and gain actionable insight.
Our Spectrum AI Model Catalog features ready-made clinical models like disease onset, hospitalization, and chronic condition management. It also includes financial models like high-cost members or patients, stop-loss, and group cost. If you prefer to build your own models, you can use our Lightning AI Model Builder, which includes healthcare-specific machine learning features that have been tested and proven with healthcare data.
Want to see how Lumiata can impact the future of pediatric care at your organization? Click here to request a demo.